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Uv workflow improvements (#531)
* Uv workflow improvements * Uv workflow improvements * linter improvements * pytproject.toml fixes * pytproject.toml fixes * pytproject.toml fixes * pytproject.toml fixes * pytproject.toml fixes * pytproject.toml fixes * windows fixes * windows fixes * windows fixes * windows fixes * windows fixes * windows fixes * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix * win32 fix
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Sebastian Raschka
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@@ -2,7 +2,7 @@
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This tutorial is an alternative to *Option 1: Using uv* in the [README.md](./README.md) document for those who prefer `uv`'s native commands over the `uv pip` interface. While `uv pip` is faster than pure `pip`, `uv`'s native interface is even faster than `uv pip` as it has less overhead and doesn't have to handle legacy support for PyPy package dependency management.
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The table below provides a comparison of the speeds of different dependency and package management approaches. The speed comparison specifically refers to package dependency resolution during installation, not the runtime performance of the installed packages. Note that ackage installation is a one-time process for this project, so it is reasonable to choose the preferred approach by overall convenience, not just installation speed.
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The table below provides a comparison of the speeds of different dependency and package management approaches. The speed comparison specifically refers to package dependency resolution during installation, not the runtime performance of the installed packages. Note that package installation is a one-time process for this project, so it is reasonable to choose the preferred approach by overall convenience, not just installation speed.
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| Command | Speed Comparison |
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@@ -74,9 +74,15 @@ To install all required packages from a `pyproject.toml` file (such as the one l
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uv add . --dev
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```
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> [!NOTE]
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> If you have problems with the following commands above due to certain dependencies (for example, if you are using Windows), you can always fall back to regular pip:
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> `uv add pip`
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> `uv run python -m pip install -U -r requirements.txt`
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<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/setup/uv-setup/uv-add.png?1" width="700" height="auto" alt="Uv install">
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Note that the `uv add` command above will create a separate virtual environment via the `.venv` subfolder.
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Note that the `uv add` command above will create a separate virtual environment via the `.venv` subfolder. (In case you want to delete your virtual environment to start from scratch, you can simply delete the `.venv` folder.)
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You can install new packages, that are not specified in the `pyproject.toml` via `uv add`, for example:
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@@ -84,58 +90,19 @@ You can install new packages, that are not specified in the `pyproject.toml` via
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uv add packaging
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```
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## Optional: Manage virtual environments manually
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Alternatively, you can still install the dependencies directly from the repository using `uv pip install`. Note that this requires creating and activating the virtual environment manually:
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<br>
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**1. Create a new virtual environment**
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Run the following command to manually create a new virtual environment, which will be saved via a new `.venv` subfolder:
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And you can remove packages via `uv remove`, for example,
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```bash
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uv venv --python=python3.10
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```
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<br>
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**2. Activate virtual environment**
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Next, we need to activate this new virtual environment.
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On macOS/Linux:
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```bash
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source .venv/bin/activate
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```
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On Windows (PowerShell):
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```bash
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.venv\Scripts\activate
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```
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<br>
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**3. Install dependencies**
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Finally, we can install dependencies from a remote location using the `uv pip` interface:
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```bash
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uv pip install -U -r https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/refs/heads/main/requirements.txt
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uv remove packaging
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```
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## 4. Run Python code
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## 3. Run Python code
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<br>
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**Finalizing the setup**
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Your environment should now be ready to run the code in the repository.
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Optionally, you can run an environment check by executing the `python_environment_check.py` script in this repository:
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@@ -181,10 +148,81 @@ You can launch a JupyterLab instance via:
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uv run jupyter lab
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```
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Or, if you manually activated the environment as described earlier, you can drop the `uv run` prefix.
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**Skipping the `uv run` command**
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If you find typing `uv run` cumbersome and want to run scripts via
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```bash
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python script.py
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```
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and launch JupyterLab via
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```bash
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juputer lab
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```
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instead, you can activated the environment manually.
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On macOS/Linux:
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```bash
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source .venv/bin/activate
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```
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On Windows (PowerShell):
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```bash
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.venv\Scripts\activate
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```
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## Optional: Manage virtual environments manually
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Alternatively, you can still install the dependencies directly from the repository using `uv pip install`. But note that this doesn't record dependencies in a `uv.lock` file as `uv add` does. Also, it requires creating and activating the virtual environment manually:
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<br>
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**1. Create a new virtual environment**
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Run the following command to manually create a new virtual environment, which will be saved via a new `.venv` subfolder:
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```bash
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uv venv --python=python3.10
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```
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<br>
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**2. Activate virtual environment**
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Next, we need to activate this new virtual environment.
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On macOS/Linux:
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```bash
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source .venv/bin/activate
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```
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On Windows (PowerShell):
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```bash
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.venv\Scripts\activate
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```
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<br>
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**3. Install dependencies**
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Finally, we can install dependencies from a remote location using the `uv pip` interface:
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```bash
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uv pip install -U -r https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/refs/heads/main/requirements.txt
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```
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---
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Any questions? Please feel free to reach out in the [Discussion Forum](https://github.com/rasbt/LLMs-from-scratch/discussions).
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